Last updated: March 2026 · Based on 14,902 verified US Starbucks locations · Population data: US Census Bureau estimates
In retail real estate and franchise development, white space refers to geographic markets that are underserved relative to demonstrated consumer demand. For coffee specifically, white space analysis asks: where does population density, income, traffic patterns, and consumer behavior suggest strong demand — but the existing store network has not yet filled?
Starbucks is the most useful benchmark in US coffee site selection for two reasons:
This page documents both: markets where Starbucks density is low relative to population and income (true white space), and the structural factors that shape those gaps.
The following markets are genuinely underserved by Starbucks relative to their population, income, and traffic characteristics. This is not a speculative list — it is derived from the location dataset and cross-referenced against US Census Bureau population estimates and household income data.
For each market, we show the approximate Starbucks count, estimated population, and the implied stores-per-resident ratio. Markets with a ratio materially worse than the national average of ~1:22,000 are flagged as high or very high opportunity.
| Market / Metro | Est. Pop. | Approx. SBUX Stores | Stores per 100K Residents | White Space Signal |
|---|---|---|---|---|
|
Jackson, MS MSA Hinds, Madison, Rankin counties |
580,000 | ~14 | 2.4 | Very High |
|
Baton Rouge, LA MSA East Baton Rouge, Ascension, Livingston parishes |
870,000 | ~24 | 2.8 | Very High |
|
Des Moines, IA MSA Polk, Dallas, Warren counties |
715,000 | ~22 | 3.1 | High |
|
Little Rock, AR MSA Pulaski, Saline, Faulkner counties |
760,000 | ~19 | 2.5 | Very High |
|
Wichita, KS MSA Sedgwick, Butler, Harvey counties |
650,000 | ~17 | 2.6 | Very High |
|
Tulsa, OK MSA Tulsa, Wagoner, Rogers counties |
1,020,000 | ~31 | 3.0 | High |
|
Mobile, AL MSA Mobile, Baldwin counties |
440,000 | ~11 | 2.5 | Very High |
|
Shreveport, LA MSA Caddo, Bossier, DeSoto parishes |
395,000 | ~9 | 2.3 | Very High |
|
Springfield, MO MSA Greene, Christian, Webster counties |
480,000 | ~12 | 2.5 | Very High |
|
Fayetteville-Springdale, AR MSA Washington, Benton counties — fast-growing market |
590,000 | ~15 | 2.5 | Very High |
Approximate store counts based on the March 2026 dataset. Population figures from US Census Bureau 2024 estimates. National average: ~4.5 Starbucks per 100,000 residents.
Several structural patterns emerge from the data across these underserved markets:
Franchise developers — specifically those building out networks for coffee, quick-service, and fast-casual concepts — use Starbucks location density as one of the most reliable proxy signals available for market validation. Here are three specific use cases backed by the dataset fields in our product:
Developers calculate Starbucks stores per square mile or per ZIP code to build a density heat map. ZIP codes with low Starbucks density but high household income and traffic counts score as top-tier development candidates. The nearest-store distance field in our dataset is the key input: a candidate site with the nearest Starbucks more than 3 miles away, in a ZIP with median income above $60K, is a textbook white space signal.
Multi-unit franchise agreements are structured around territory boundaries. Developers use Starbucks density as an independent third-party signal to define defensible territory sizes — areas large enough to build multiple units without cannibalization. In white space markets, franchisee territories tend to be larger, which increases the per-territory value for well-capitalized operators.
Our dataset flags every Starbucks location with its drive-through status and ownership type (company-operated vs. licensed). In white space markets, developers look for areas where Starbucks is present but without drive-throughs — a validated demand signal for the format. A market where the local Starbucks is licensed (in a grocery or campus setting) with no company-operated drive-through within 5 miles is a high-value opportunity for a competing drive-through coffee brand.
For quick-service restaurant real estate teams, Starbucks location data is used as a validated demand proxy, not just a competitive benchmark. This distinction matters: Starbucks' presence in a trade area tells you that the daytime traffic, income demographics, and consumer behavior support premium morning occasion purchases. Their absence in an otherwise-viable market tells you something different — either demand is lower than it appears, or the market is genuinely undercaptured.
QSR expansion teams — particularly those building drive-through-first formats — use Starbucks drive-through density as their primary co-tenancy signal. The logic is straightforward:
has_drive_through boolean field on every US location — precisely the input that powers this analysis.
The full US dataset — all 14,902 Starbucks locations with 20 fields including GPS coordinates, drive-through status, ownership type, operating hours, and nearest-store distance — is available for immediate download.
| Dataset | Coverage | Price | Delivery |
|---|---|---|---|
| US Full Dataset | 14,902 stores, all 50 states | $49 | CSV + Excel, instant download |
| Global Dataset | 33,414 stores, 80 countries | $149 | CSV + Excel, instant download |
| REST API | All countries, all fields, real-time | from $29/mo | JSON, filter by any field |
We define white space as markets where the ratio of Starbucks stores to resident population is materially below the US national average of approximately 4.5 stores per 100,000 residents, after controlling for income and urban density. We exclude low-income, low-density rural geographies from the opportunity ranking because below a population density threshold, even underserved markets may not support standalone coffee units. The markets listed here are mid-sized to large metros with commercial infrastructure sufficient to support QSR-format coffee.
Yes. The dataset was updated March 2026 and reflects current open/closed status for all 14,902 US locations. Unlike free datasets on GitHub or Kaggle — which are typically from 2021–2023 — our data captures recent openings, closures, and format changes. We update quarterly. If you need a specific update date for a report or investment memo, it is available on the dataset's metadata sheet.
Yes — and this is one of the highest-value use cases for the dataset. Because every Starbucks location includes latitude and longitude to 6 decimal places, you can calculate the Haversine distance from any candidate site to the nearest Starbucks in seconds using Python, R, Excel, or any GIS tool. The dataset also includes a pre-calculated nearest_store_distance_mi field for each store, giving you a ready-made density signal without any computation.
Yes. Every record includes an ownership_type field: CO (company-operated) or LS (licensed store). This distinction is critical for white space analysis because licensed stores — typically in grocery stores, airports, universities, and hospitals — operate under different economics and serve captive audiences rather than open trade areas. For drive-through and franchise site selection, company-operated Starbucks locations are the relevant competitive signal.
Yes. If you need a custom white space report, density heat map, or ranked list of sites for a specific metro or state, contact data@starbucks-locations.com. We also offer enterprise team licenses for real estate teams that need multiple users or ongoing API access. The REST API starting at $29/month provides programmatic access to the full dataset with filters for country, state, city, drive-through status, and ownership type.
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For questions about the dataset, bulk licensing, or custom analysis: data@starbucks-locations.com